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# Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)¶

## Module Interface¶

class torchmetrics.audio.ScaleInvariantSignalDistortionRatio(zero_mean=False, **kwargs)[source]

The SI-SDR value is in general considered an overall measure of how good a source sound.

As input to forward and update the metric accepts the following input

• preds (Tensor): float tensor with shape (...,time)

• target (Tensor): float tensor with shape (...,time)

As output of forward and compute the metric returns the following output

• si_sdr (Tensor): float scalar tensor with average SI-SDR value over samples

Parameters:
Raises:

TypeError – if target and preds have a different shape

Example

>>> from torch import tensor
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> si_sdr = ScaleInvariantSignalDistortionRatio()
>>> si_sdr(preds, target)
tensor(18.4030)
plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = torch.randn(5)
>>> preds = torch.randn(5)
>>> metric = ScaleInvariantSignalDistortionRatio()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = torch.randn(5)
>>> preds = torch.randn(5)
>>> metric = ScaleInvariantSignalDistortionRatio()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

## Functional Interface¶

torchmetrics.functional.audio.scale_invariant_signal_distortion_ratio(preds, target, zero_mean=False)[source]

The SI-SDR value is in general considered an overall measure of how good a source sound.

Parameters:
• preds (Tensor) – float tensor with shape (...,time)

• target (Tensor) – float tensor with shape (...,time)

• zero_mean (bool) – If to zero mean target and preds or not

Return type:

Tensor

Returns:

Float tensor with shape (...,) of SDR values per sample

Raises:

RuntimeError – If preds and target does not have the same shape

Example

>>> from torchmetrics.functional.audio import scale_invariant_signal_distortion_ratio
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> scale_invariant_signal_distortion_ratio(preds, target)
tensor(18.4030)